Method and system for determining whether a barcode is genuine using a deviation from a nominal shape

ABSTRACT

A method for determining whether a candidate barcode is genuine involves acquiring an image of an original barcode; determining, from the image of the original barcode, a deviation of a continuous edge of the original barcode from a nominal shape; encoding the deviation as signature data for the original barcode; storing the signature data for the original barcode on a storage device; acquiring an image of the candidate barcode; determining, from the image of the candidate barcode, a deviation of a continuous edge of the candidate barcode from the nominal shape; retrieving the signature data for the original barcode from the storage device; comparing the signature data for the original barcode with the signature data for the candidate barcode; and making a determination that the candidate barcode is genuine or not genuine based on a result of the comparison.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/528,907, filed Aug. 1, 2019, now U.S. Pat. No. 10,552,848,which is a continuation of U.S. patent application Ser. No. 15/491,523,filed Apr. 19, 2017, now U.S. Pat. No. 10,380,601, which is acontinuation of U.S. patent application Ser. No. 14/561,215, filed Dec.4, 2014, now abandoned, which is a continuation of U.S. patentapplication Ser. No. 13/782,233, filed Mar. 1, 2013, now U.S. Pat. No.8,950,662, which claims priority to U.S. Provisional Patent ApplicationNo. 61/605,369, filed Mar. 1, 2012; U.S. Provisional Patent ApplicationNo. 61/676,113, filed Jul. 26, 2012; and U.S. Provisional PatentApplication No. 61/717,711, filed Oct. 24, 2012. The disclosures of eachof these applications are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure is generally directed to machine vision devicesand methods and, more particularly, the use of such devices and methodsfor verifying items.

BACKGROUND

Some currently existing methods of uniquely identifying items are basedon overt or covert marks deliberately applied to items, usually byprinting. Other methods rely on natural variations in a materialsubstrate (fiber orientation in paper for example) to be used as aunique identifier. Current methods have significant deficiencies,however. These include the need to deliberately add overt or covertmarks to the item in addition to any marks already present on the itemfor other purposes. The substrate variation method, for example,requires a specialized system that perceives the variations to benecessary. Also, for substrates that do not present a readilyidentifiable unique feature (some plastic films, for example) thismethod cannot be employed. These deficiencies seriously reduce theutility of these methods in the technical fields considered herein.

DRAWINGS

While the appended claims set forth the features of the presenttechniques with particularity, these techniques may be best understoodfrom the following detailed description taken in conjunction with theaccompanying drawings of which:

FIG. 1 is an illustration of an instance of a printed mark made use ofby methods embodying the present disclosure.

FIG. 2 is an illustration of the mark in FIG. 1 with the mark's edgefeatures extracted for clarity.

FIG. 3 is an illustration of a second instance of the same mark as inFIG. 1, which may represent a counterfeit version of the mark in FIG. 1.

FIG. 4 is an illustration of the mark in FIG. 3 with the mark's edgefeatures extracted for clarity.

FIG. 5 is a 2-D data matrix illustrating some features that may be usedin the present disclosure.

FIG. 6 is an illustration comparing the features of the upper leftsections of FIG. 2 and FIG. 4.

FIG. 7 is a schematic diagram of a computer system.

FIG. 8 is a block diagram of a computer system operative to carry outthe process of embodiments of the disclosure.

FIG. 9 is a flow chart of an embodiment of a method of recording a newmark.

FIG. 10 is a diagram of the weighting of characteristic features.

FIG. 11 is a flow chart of an embodiment of a method of evaluating amark.

FIG. 12 is a 1-D barcode illustrating some features that may be used inthe present disclosure.

FIG. 13 is a graph of a polynomial approximation of an autocorrelationseries for a genuine item with a genuine “candidate” symbol.

FIG. 14 is a chart of a power series for the genuine data in FIG. 13.

FIG. 15 is a chart similar to FIG. 14 for the “candidate” data in FIG.13.

FIG. 16 is a graph similar to FIG. 14 for a counterfeit “candidate”symbol.

FIG. 17 is a chart similar to FIG. 14 for the counterfeit data used inFIG. 16.

FIG. 18 is a diagram of part of a 2-D data matrix, illustrating anencoding process.

DESCRIPTION

Turning to the drawings, wherein like reference numerals refer to likeelements, techniques of the present disclosure are illustrated as beingimplemented in a suitable environment. The following description isbased on embodiments of the claims and should not be taken as limitingthe claims with regard to alternative embodiments that are notexplicitly described herein.

The present disclosure teaches utilizing natural variations in markedfeatures on an item as a way of establishing information or dataspecific to that item, which may be referred to as a “signature” or an“original item identifier,” storing the information separately from theitem, and subsequently accessing the stored information to validate theidentity of an item that is alleged to be the original item. Thedeliberate application of covert or overt identifying marks on the itemis not required, although it can be used in some embodiments. Instead,the natural variations inherent in many manufacturing, marking, orprinting processes can be exploited to extract identifying features ofan item or a mark, such as one of many types of marks applied to items.Further, this approach easily integrates into existing reader systemsfor applied marks, such as bar code readers or machine vision systems;no specialized systems are needed to perceive variations in a materialsubstrate sufficient to identify an item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying an identity of an item, comprising: examining anoriginal item for original artifacts specific to the original item;extracting information associated with the original artifacts; rankingthe information according to a characteristic of the respectiveartifact; and storing the ranked information in a non-transitorycomputer readable storage device separate from the original item.

The artifacts may be features of the item that were produced when theitem was produced. At least some of the artifacts may be notcontrollably producible in producing the item. The characteristic bywhich the original artifacts are ranked may be a magnitude, for example,a size of an artifact. The ranked original artifacts' information(information associated with the original artifacts) may be encoded intocomputer readable data corresponding to the original item to form asignature.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying an identity of an item, comprising: examining amark that comprises an identifier and an artifact, wherein theidentifier is associated with an original item and the artifact does notalter the association; extracting information associated with theartifact; and storing the information in a non-transitory computerreadable storage device separate from the original item so as to be atleast partially locatable using the identifier.

Respective information from a plurality of said marks may be stored inone storage device, for example in the form of a database, and using theidentifier from one of said marks, the respective information from anumber of marks smaller than said plurality of marks and comprising saidone mark may be retrievable. In an example, the identifier may identifya group or category of items. The identifier can then be used toretrieve from the database only the stored information relating to itemsin that group or category, reducing the extent of a subsequent search toidentify the information on a single item. In another example, thesmaller number of marks may be only the one mark. For example, theidentifier may be a Unique Identifier (UID) that explicitly identifiesonly a single item, and the information may be stored so as to beretrievable using the UID.

Embodiments of the present invention provide methods, apparatus, andcomputer programs (which may be stored on a non-transitory tangiblestorage medium) for verifying an identity of an item, comprising:examining an original item for original artifacts specific to theoriginal item; storing information associated with the originalartifacts and information associated with at least one of apparatusinvolved in creating the original artifacts and apparatus involved inexamining the original item in a non-transitory computer readablestorage device separate from the original item.

The stored information may include information indicative of a type ofthe apparatus involved in creating the original artifacts. The storedinformation may include information indicative of a resolution of theapparatus involved in examining the original item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: examining anunverified item for unverified artifacts specific to the unverifieditem; extracting information representing the unverified artifacts;retrieving from a storage device stored data containing informationrepresenting original artifacts of an original item; recovering theoriginal artifacts' information from the stored data; comparing theunverified and original artifacts' information to determine whether theunverified artifacts' information (information associated with theunverified artifacts) matches the original artifacts' information; andin the case the unverified artifacts' information matches the originalartifacts' information, verifying the unverified item as a verifiedoriginal item; wherein the comparing includes correcting for propertiesof at least one of apparatus involved in the creation of the originalartifacts, apparatus involved in the examination of the original itemfor the information representing the original artifacts, and apparatusinvolved in the examination of the unverified item for the informationrepresenting the unverified artifacts.

The stored data may include information relating to at least one of theapparatus involved in the creation of the original artifacts and theapparatus involved in the examination of the original item.

The correcting may comprise comparing resolutions or other properties ofthe apparatus involved in examining the original item and the apparatusinvolved in examining the unverified item, and discounting artifactsdetected by one of those apparatuses that would not be reliably detectedby the other of those apparatuses. Where the two apparatuses havedifferent resolutions, artifacts that are larger than the resolutionlimit of one apparatus, and are detected by that apparatus, but aresmaller than the resolution limit of the other apparatus, may bediscounted. The weighting may be based on a characteristic resolvingpower and imaging fidelity of the verification device versuscorresponding characteristics of the original imaging device.

Where the artifacts are of distinct categories, determining whether theunverified artifacts' information matches the original artifacts'information may comprise comparing the detected artifacts in eachcategory and combining the results of the comparisons, and thecorrecting may then comprise weighting the combining according to aknown tendency of the apparatus that created the original artifacts toproduce artifacts in different categories with different frequencies ordifferent magnitudes.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: examining anoriginal item having on it an original symbol comprising an array ofdifferently colored printed cells for original artifacts specific to theoriginal symbol, wherein: the artifacts are features of at least some ofthe cells that were produced when the original symbol was produced; andat least some of the artifacts were not controllably producible inproducing the original symbol; and the artifacts comprise at least onecategory of artifact selected from the group consisting of deviation inaverage color of a cell from an average derived from within the mark,which may be an average for neighboring cells of the same nominal color,bias in position of a cell relative to a best-fit grid of neighboringcells, areas of a different one of at least two colors from a nominalcolor of the cells, and deviation from a nominal shape of a longcontinuous edge; extracting information representing the originalartifacts for each cell; encoding the original artifacts' informationinto computer readable data corresponding to the original item; andstoring the data in a non-transitory computer readable storage deviceseparate from the original item.

In general, different “colors” may differ in lightness, hue, or both,and may be distinguished by differences in lightness, hue, or both. Forexample, where the symbol is printed in an ink or other medium of asingle first color on a substrate of a single second color, any measurethat distinguishes the first color from the second color may be used. Inthe commonest case, commonly called “black and white” or “monochrome,”the printing medium is blackish, the paper is whitish, and a differencein albedo is used to distinguish them. However, in other circumstances,for example, in printing with more than one color of ink, it may bedesirable or even necessary to measure differences in hue instead of, orin addition to, differences in brightness.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: examining anoriginal item for original artifacts specific to the original item;extracting information associated with the original artifacts; rankingthe original artifacts' information according to a characteristic of theartifact; calculating an autocorrelation series of the ranked originalartifacts' information; and storing data related to the autocorrelationseries in a non-transitory computer readable storage device separatefrom the original item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, by: examining an originalitem for original artifacts specific to the original item, wherein theartifacts are features of the item that were produced when the item wasproduced, and at least some of the artifacts were not controllablyproducible in producing the item; extracting information representingthe original artifacts; encoding the original artifacts' informationinto computer readable data corresponding to the original item; andstoring the data in a non-transitory computer readable storage deviceseparate from the original item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: defining aplurality of modules on an original item and an order of the modules;examining the modules on the original item for a plurality of categoriesof original artifacts specific to the original item, wherein theartifacts are features of the item that were produced when the item wasproduced, and at least some of the artifacts were not controllablyproducible in producing the item; extracting information representingthe original artifacts; encoding for each module in order whichcategories of artifact are present and which categories of artifact areabsent to form computer-readable data; and storing the data in anon-transitory computer readable storage device separate from theoriginal item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: examining anunverified item for one or more unverified artifacts specific to theunverified item; extracting information representing the unverifiedartifacts; retrieving stored data relating to one or more artifacts ofan original item from a storage device; recovering the originalartifacts' information from the retrieved stored data; comparing theunverified and original artifacts' information to determine whether theunverified artifacts' information matches the original artifacts'information; and in the case the unverified artifacts' informationmatches the original artifacts' information, verifying the unverifieditem as a verified original item. The processing of the unverified item,or the processing of the original item, is in accordance with any of theaspects and embodiments of the present invention. It may be preferred toprocess both the unverified and the original item by fairly similarprocesses, to reduce the level of error and uncertainty introduced bydifferences between the processes used.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, comprising: examining anunverified item for unverified artifacts specific to the unverifieditem; extracting information representing the unverified artifacts;ranking the unverified artifacts' information according to acharacteristic of the artifact; calculating an autocorrelation series ofthe ranked unverified artifacts' information; retrieving anautocorrelation series representing artifacts of an original item from astorage device; comparing the unverified and original autocorrelationseries to determine whether the unverified artifacts' informationmatches the original artifacts' information; and in the case theunverified artifacts' information matches the original artifacts'information, verifying the unverified item as a verified original item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, by: examining anunverified item for unverified artifacts specific to the unverifieditem; extracting information representing the unverified artifacts;retrieving data comprising original artifacts' information from astorage device; recovering original artifacts' information from theretrieved data; comparing the unverified and original artifacts'information; and in the case the unverified artifacts' informationmatches the original artifacts' information, verifying the unverifieditem as a verified original item.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item, by successively carryingout any of the above processes for generating and storing data orinformation, and any appropriate above process for comparing anunverified item with stored data or information.

Embodiments of the disclosure provide methods, apparatus, and computerprograms (which may be stored on a non-transitory tangible storagemedium) for verifying the identity of an item combining features of anytwo or more of the above methods, apparatus, and computer programs.

Ranking the original artifacts' information may include treatingartifacts with a characteristic below a threshold value differently fromartifacts above the threshold. For example, artifacts smaller than thethreshold may not be ranked, or may be grouped together with locationswhere no artifact is detected, or may be discounted. The threshold maybe chosen with consideration for a noise threshold of the artifacts andthe apparatus used to detect them, below which artifacts cannot reliablybe detected or cannot reliably be quantified. In an embodiment, rankingmay consist simply of separating artifacts above the threshold fromartifacts below the threshold or entirely absent. However, in manyembodiments it is preferred that the characteristic is quantifiable andthe ranking comprises ordering the artifacts according to a magnitude orquantity of the characteristic.

The method may comprise extracting information representing a pluralityof different categories of artifact, and ranking may then compriseranking the original artifacts' information separately for each categoryof artifact.

The method may comprise defining a plurality of predetermined locationson the original item, and extracting information representing theartifacts may then comprise associating each artifact with one of thepredetermined locations. Wherein the original item bears a printedsymbol comprising a plurality of cells, the predetermined locations maybe at least some of the plurality of cells, and the artifacts may thenbe artifacts of the printing of the cells. Where practical, it isusually preferred to use the entire symbol, in order to maximize thenumber of available artifacts. However, that is not always necessary.For example, if the symbol has a large number of cells and a highincidence of usable artifacts, a smaller group of cells may be used. Inan embodiment, six categories of artifact, with 100 artifacts of eachcategory, ranked by magnitude within each category, have been found togive a robust result.

The artifacts may comprise at least one category of artifact selectedfrom the group of categories consisting of deviation in average color ofa cell from the average for neighboring cells of the same nominal color;bias in position of a cell relative to a best-fit grid of neighboringcells; areas of a different color from a nominal color of the cellwithin which they appear; and deviation from a nominal shape of a longcontinuous edge.

Producing the original item may comprise applying a mark to the originalitem, and the artifacts may then be features of the mark. “Producing theoriginal item” may include every stage before examining begins, and themark may be applied in a separate step at any time between whenproduction of the original item begins and immediately before examining.

Where the item or mark is printed, the artifacts may compriseimperfections or other variations in printing. Where the printed markconveys information, the imperfections may be too small to materiallyaffect the readability of the information. Producing the item mayfurther comprise causing additional random or quasi-random featuresusable as said artifacts to be produced in the printing. The extractingof information may further comprise determining a type of printer usedin producing the artifacts, where the artifacts are of a plurality ofdistinct categories. Encoding the ranked original artifacts' informationand storing may then comprise at least one of ranking differentcategories of artifacts according to the type of printer, and storingdata indicating the type of printer as part of the stored data. Theinformation may be useful, because different types of printers canproduce different categories of artifacts with different magnituderanges, more or less frequently, or with other variations that mayaffect how to assess or how much weight to give to different categoriesof artifact.

Other information relating to the original item may be incorporated inthe stored data in addition to the information representing the originalartifacts. The other original item information may include a serialnumber specific to the original item. Such other information may then berecovered from the retrieved stored data additionally to the informationrepresenting the original artifacts.

Where at least some of the artifacts are artifacts of a symbol thatencodes data, and the encoded data include a UID for an individualinstance of the symbol or other identifying data, the stored data may bestored so as to be retrievable under an identifier derivable from theUID or other identifying data. Where the other identifying data onlypartially identifies the symbol, for example, identifies a category orgroup of items smaller than all the items for which data is stored in adatabase, the data may be stored so that the stored data for thecategory or group are retrievable under an identifier derivable from theother identifying data. The stored data for a desired individualoriginal item may then be retrieved by a further search within theretrieved group.

Determining may comprise assessing a statistical probability that theunverified artifacts' information matches the original artifacts'information. It may then be determined that the unverified artifacts'information matches the original artifacts' information when theunverified artifacts' information and the original artifacts'information are within a predetermined percentage of each other.

In the case the statistical probability exceeds a first threshold, itmay be determined that the unverified item is a verified original item.In the case the statistical probability is below a second thresholdlower than the first threshold, it may be determined that the unverifieditem is not an original item. In the case the statistical probability isbetween the first and second thresholds, it may then be reported that itcannot be determined whether the unverified item is an original item.

In assessing the statistical probability, greater weight may be given toartifacts of greater magnitude.

Comparing the artifacts' information may include detecting artifactsthat are present in one of the original item and the unverified item,and absent in the other of the original item and the unverified item.The presence of an artifact in the unverified item that was not presentin the original item, absent an indication that the item has beendamaged in the meantime, may be as significant as the presence of anartifact in the original item that is not present in the unverifieditem.

In general, “discounting” an artifact includes considering that artifactwith lower statistical ranking than otherwise comparable artifacts,considering that artifact in a separate class of artifacts that cannotbe accurately quantified and/or ranked, considering that artifact in thesame way as a location with no detected artifact of that category, andtotally ignoring that artifact. Different ones of those approaches maybe applied at different points even within a single embodiment.

Where at least some of the artifacts are artifacts of a symbol thatencodes data and supports error detection, extracting informationrepresenting the unverified artifacts may include determining an errorstate of the symbol containing the unverified artifacts. Where the errorstate indicates that part of the symbol is damaged, the comparing maythen comprise discounting artifacts in the damaged part of the symbol.

Prior to the storing step, the original item may be partitioned into aplurality of original zones. Each of at least a portion of the originalartifacts may then be associated with the original zone in which it islocated. Information representing the associated original artifacts andtheir respective original zones in the stored data may be preserved. Theunverified item may then be partitioned into at least one availableunverified zone corresponding to fewer than all of the original zones.Each of at least a portion of the unverified artifacts may be associatedwith the available unverified zone in which it is located. Informationrepresenting the original artifacts and the associated original zonesthat correspond to the available unverified zones may be recovered fromthe retrieved stored data. In the comparing step, only the informationrepresenting the original artifacts and the associated original zonesthat correspond to the available unverified zones may be used.

The original item may be attached to an object to form an originalobject, prior to the step of examining an unverified item; and in thecase the unverified artifacts' information matches the originalartifacts' information, an object to which the unverified item isattached may then be verified as a verified original object.

The magnitude of a deviation in average color may be normalized byreference to a difference between average colors for neighboring cellsof at least two nominal colors. The magnitude of bias in position of acell relative to a best-fit grid of neighboring cells may be normalizedby reference to the size of the cells. The magnitude of areas of theopposite color from a nominal color of the cells may be determined bythe size of the areas, normalized by reference to the size of the cells.The magnitude of deviation from a nominal shape of a long continuousedge may be normalized by reference to a best fit straight line or othersmooth curve.

Where encoding the ranked original artifacts' information comprisescalculating an autocorrelation series of the ranked original artifacts'information, encoding may further comprise representing or approximatingthe autocorrelation series as a polynomial to a fixed order and usingthe polynomial coefficients to form the stored data. The approximationmay be to a polynomial of a predetermined order, and the coefficientsmay be approximated to a predetermined precision.

Where encoding the ranked original artifacts' information comprisescalculating an autocorrelation series of the ranked original artifacts'information, comparing may comprise calculating an autocorrelationseries of the unverified artifacts' information, and comparing the twoautocorrelation series. Comparing may further or alternatively comprisecomparing Discrete Fourier Transform (DFT) power series of the twoautocorrelation series, and may then comprise comparing at least one ofthe Kurtosis and Distribution Bias functions of the DFT power series.

According to embodiments of the invention, there is provided anapparatus or system for verifying the identity of an item, comprising:an original item scanner operable to examine an original item andextract information representing original artifacts of the originalitem, by the method of any one or more of the mentioned embodiments andaspects of the invention; an encoder operable to encode the extractedinformation into a computer readable item identifier; and a computerreadable storage device operable to store the item identifier.

According to embodiments of the disclosure, there is provided anapparatus or system for verifying the identity of an item by the methodof any one or more of the mentioned embodiments and aspects of theinvention, comprising: a verifying scanner operable to examine anunverified item and extract information representing unverifiedartifacts of the unverified item; and a processor operable to retrieve astored item identifier from a storage device, recover originalartifacts' information from the retrieved item identifier, compare theunverified and original artifacts' information, and produce an outputdependent on the result of the comparison.

According to embodiments of the disclosure, there is provided anapparatus or system for verifying the identity of an item, comprising incombination the above described apparatus or system for creating andstoring an item identifier, and the above described apparatus or systemfor examining and comparing an unverified item.

The verifying scanner may be coupled to a point of sale device. Theverifying scanner may be embodied in a cell phone.

The system may further comprise an original item producer operable toproduce an original item, wherein the artifacts are features of the itemthat are produced when the original item producer produces the item, andat least some of the artifacts are not controllably producible by theoriginal item producer.

The original item producer may be operative to intentionally produce orenhance at least some of the artifacts.

The original item producer may comprise an original mark applier thatapplies a mark to the original item, with the artifacts then beingfeatures of the mark.

The original item producer may comprise a printer, with at least some ofthe artifacts then comprising variations or imperfections in theprinting.

The system may further comprise at least one original item for which theitem identifier is stored in the computer readable storage device.

In various embodiments, the artifacts may be features of the itemitself, or of a mark that has been applied to the item. The item may bethe thing that is ultimately to be verified, or may be appended(typically but not necessarily in the form of a label) to an object thatis to be verified. Where the object, the item, or the mark involvesprinting, some or all of the artifacts may be variations orimperfections in the printing. “Verifying the identity of an item” mayinclude verifying that printing or other mark applied to an item, or anitem appended to an object, has not been altered or replaced, even ifthe underlying item or object is original. For example, it may bedesired to verify that an expiry date, serial number, or other trackingor identification data has not been tampered with.

In many embodiments, it is preferred that the artifacts be features thatdo not affect, or at least do not diminish, the function or commercialvalue of the mark, item, or object in which they appear.

Another aspect of the disclosure provides original items, includingoriginal objects comprising objects to which original items have beenattached, for which signature data have been stored in the storagedevice of a system according to another aspect of the invention.

An embodiment of the disclosure is a method that operates on marks thatare applied to items. These marks may be for the purpose of uniquelyidentifying an item, as with a serial number, for example, or they maybe marks that are used for other purposes, such as branding, labeling ordecoration. These marks may be printed, etched, molded, formed,transferred, or otherwise applied to the item using various processes.The marks are acquired such that they can be processed in electronicform. Possible devices that may be used for electronic acquisition ofthe marks include machine vision cameras, bar code readers, line scanimagers, flatbed scanners, and hand-held portable imaging devices.

Referring now to the drawings, in FIG. 1 there is shown an example of aprinted mark 20 to which various methods described herein may beapplied. In this example the printed mark 20 is a 2-dimensional barcode.This barcode is a data-carrier of information, where the information isencoded as a pattern of light areas 22 and dark areas 24 of the printedmark 20. A possible implementation of the 2-D barcode includes arectangular grid, with each cell or “module” 22, 24 in the grid eitherblack or white, representing a bit of data.

FIG. 2 provides an enhanced view of some of the variations present inthe mark shown in FIG. 1. FIG. 2 shows only the edges 26 between lightand dark areas of the mark shown in FIG. 1. Features such as edgelinearity, region discontinuities, and feature shape within the markshown in FIG. 1 are readily apparent. Numerous irregularities along theedges of the mark's printed features are clearly visible. Note that thisillustration is provided for clarity and is not necessarily a requiredprocessing step. In some embodiments, such edge extraction is beneficialand therefore utilized. In some embodiments, features other than edgesare extracted.

FIG. 3 shows an example of a second printed mark 30, which may representa counterfeit of the mark 20 shown in FIG. 1, or may represent a secondunique instance of the mark for identification purposes. This secondprinted mark 30 is also a 2-dimensional barcode. This counterfeitbarcode 30, when read with a 2-dimensional barcode reader, presentsexactly the same decoded information as the mark 20 of FIG. 1. When themark 30 of FIG. 3 is acquired, in an embodiment, significant featuresare identified and captured as “signature” data that uniquely identifiesthe mark. As in the case of FIG. 1, this signature data is derived fromthe physical and optical characteristics of the mark's geometry andappearance and can include data that is encoded in the mark (e.g., ifthe mark is a data-carrying symbol such as a 2-dimensional barcode). Theproperties of the mark evaluated for creating the signature data areusually the same properties used in evaluating the first instance of themark, so that the two signatures are directly comparable.

FIG. 4 provides an enhanced view of some of the variations present inthe mark 30 shown in FIG. 3. FIG. 4 shows only the edges 32 of the markshown in FIG. 3, similarly to FIG. 2. The corresponding features andvariations, such as edge linearity, region discontinuities, and featureshape within the mark shown in FIG. 3 are readily apparent. Examples ofsome of the features that may be used are shown in more detail in FIG.5, which is discussed in more detail below.

FIG. 6 shows a close comparison of the upper left corner features ofFIG. 2 and FIG. 4. As may be seen most clearly in FIG. 6, the twoprinted marks 20, 30 of FIGS. 1 and 3, even though identical in respectof their overtly coded data, contain numerous differences on a finerscale, resulting from the imperfections of the printing process used toapply the marks. These differences are durable, usually almost asdurable as the mark itself, and are practically unique, especially whena large number of differences that can be found between the symbols ofFIG. 1 and FIG. 3 are combined. Further, the differences are difficult,if not almost impossible, to counterfeit, because the original symbolwould have to be imaged and reprinted at a resolution much higher thanthe original printing, while not introducing new distinguishableprinting imperfections. While only the upper left corner section of themarks is shown here, differentiable features between the two marks shownin FIGS. 1 and 3 run throughout the entirety of the marks and can beutilized according to various embodiments.

Referring to FIG. 7, an embodiment of a computing system 50 comprises,among other equipment, a processor or CPU 52, input and output devices54, 56, including an image acquisition device 58, random access memory(RAM) 60, read-only memory (ROM) 62, and magnetic disks or otherlong-term storage for programs and data. The computing system 50 mayhave a printer 65 for generating marks 20, or the printer 65 may be aseparate device. The computing system 50 may be connected through aninterface 66 to an external network 68 or other communications media,and through the network 68 to a server 70 with a long-term storage 72.Although not shown in the interests of simplicity, several similarcomputer systems 20 may be connected to server 70 over network 68.

Referring to FIG. 8, in an embodiment, the image acquisition device 58supplies image data to a signature extraction and encoding processor 74,which may be software running on the CPU 52 of computer system 50, ormay be a dedicated co-processor. The signature extraction and encodingprocessor 74 supplies signature data to a network-accessible marksignature data storage 76, which may be the long-term storage 72 of theserver 70. A network-accessible mark signature look-up engine 78, whichmay be software running on the CPU 52 of computer system 50, or may be adedicated co-processor, receives signature data from the signatureextraction and encoding processor 74 and/or the signature data storage76. A signature comparison processor 80 usually compares a signatureextracted by the signature extraction and encoding processor 74 from arecently scanned mark 30 with a signature previously stored in thesignature data storage 76 and associated with a genuine mark 20. Asshown symbolically by the separation between the upper part of FIG. 8,relating to genuine mark signature capture and storage, and the lowerpart of FIG. 8, relating to candidate mark signature capture,comparison, and verification, the computer system 50 that scans thecandidate mark 30 may be different from the computer system 50 thatscanned the original mark 20. If they are different, then they may shareaccess to the signature data storage 76, or a copy of the storedsignature data may be passed from the signature data storage 76 on thegenuine mark capture system 50 to the candidate mark evaluation system50.

In more detail, and referring to FIG. 9, in an embodiment, in step 102 amark, which in this example is illustrated as a 2-D barcode similar tothat shown in FIG. 1, is applied to an object, or to a label that issubsequently applied to an object, by a printer 65. As has already beenexplained, a printer applying a 2-D barcode typically introduces asignificant number of artifacts that are too small to affect thereadability of the overt data coded by the barcode, and are too smallfor their appearance to be controllable in the printing process, but arevisible (possibly only under magnification) and durable. If a particularprinter does not naturally produce a sufficient number of artifacts,some printers can be configured to include random or pseudorandomvariations in their output, as is further discussed below.

In step 104, the mark is acquired by a suitable imaging or other dataacquisition device, such as the image acquisition device 58. The imagingdevice that acquires the mark may be of any expedient form, including aconventional device or a device hereafter to be developed. In thisembodiment, the imaging device gathers data on the appearance of themark at a level of detail considerably finer than the controllableoutput of the device that applied the mark. In the example shown inFIGS. 1-4, the detail is the shape of the boundaries between light anddark areas at a resolution considerably finer than the size of themodules of the printed 2-D barcode. Other examples of suitable featuresare described below. If the mark is being used as an anti-counterfeitingmeasure, it is strongest if the imaging device gathers data at a levelof detail finer than the controllable output of a device that is likelyto be used to apply a counterfeit mark. However, that is not necessaryif it is possible to keep secret the fact that particular details in aparticular mark are being used for that purpose.

In step 106, a UID included in the overt data of mark 20 is decoded. Ifthe printer 65 is on the same computer system 50 as the imageacquisition device 58, the UID may be passed from one to the other,avoiding the need to decode the UID from the image acquired by imageacquisition device 58. If the mark 20 does not include a UID, some otherinformation uniquely identifying the specific instance of mark 20 may beused in this step.

In steps 110 and 112, the image of the mark 20 is analyzed by thesignature extraction and encoding processor 74 to identify significantfeatures. In step 120, data relating to those features will then bestored in the signature data storage 76 as “signature” data thatuniquely identifies the mark 20. This signature data is derived from thephysical and optical characteristics of the mark's geometry andappearance, and in addition, can include data that is encoded in themark, should the mark be a data-carrying symbol such as a 2-dimensionalbarcode. The properties of the mark evaluated for creating the signaturedata can include, but are not limited to, feature shape, featurecontrast, edge linearity, region discontinuities, extraneous marks,printing defects, color, pigmentation, contrast variations, featureaspect ratios, feature locations, and feature size.

Referring now to FIG. 5, in the following example, deviation in anaverage module pigmentation or marking intensity 92, a module positionbias 94 relative to a best-fit grid, the presence or location ofextraneous marks or voids 96 in the symbol, and the shape (linearity) oflong continuous edges 98 in the symbol are used as exemplary variablefeatures. These act as the primary metrics forming the unique symbolsignature. Illustrations of some of these features are shown in FIG. 5.

In the case of the mark being a data-carrying symbol, such as a2-dimensional barcode, the present embodiment can take advantage of theadditional information embodied by and encoded into the symbol. Theinformation that is encoded, for example a unique or non-unique serialnumber, itself may then be included as part of the signature data orused to index the signature data for easier retrieval.

Further, in the case of a 2-dimensional barcode or other data carrierfor which a quality measure can be established, in step 108 informationrepresenting the quality of the symbol can optionally be extracted andincluded as part of the signature data.

The quality information can be used to detect changes to the mark 20that might cause a false determination of the mark as counterfeit, asthese changes can alter the signature data of the mark. Some of thequality measurements that can be used are, but are not limited to,Unused Error Correction and Fixed Pattern Damage as defined in ISO spec15415 “Data Matrix Grading processes” or other comparable standard.These measures make it possible to detect areas that would contributesignature data that has been altered by damage to the mark and thusdiscount it from consideration when comparing a mark's signature dataagainst the stored signature data of the genuine mark.

Signature Metrics Weighting

In this example, the ease with which each of the four metricsillustrated in FIG. 5 can be extracted depends on the imagingresolution, and the metrics can be arranged in order of the resolutionrequired to extract useful data relating to each of the four metrics, asshown in FIG. 10. In order from lowest to highest resolution, those are:module pigmentation, module position bias, void/mark location, and edgeshape projection.

Increasing image fidelity and resolution allows for increasingly preciseanalysis, making use of the progressively higher precision analytics.For example, in a low resolution image, perhaps only module averagepigmentation 92 and module position bias 94 can be extracted withsignificant confidence, so those results are given more weight indetermining the signature match of a candidate symbol against storedgenuine data. With a high resolution image, processing can continue allthe way up to the fine edge projection metric 98 and use that as thehighest weight consideration in signature match determination. If thereare disagreements with the expected signature among other (lower weight)measures, these may be due to symbol damage or artifacts of the imagecapture device. However, damage, alteration of the symbol 20, or imagerartifacts are generally not likely to modify a counterfeit code 30 tocoincidently match with high precision the edge projection signaturemetric 98 of the valid item 20. Therefore, the edge projection, ifhighly correlated and exhibiting adequate magnitude in dynamic range,can supersede the lower-resolution metrics in support of a high matchconfidence.

Further, in an embodiment, the use of Error Correction information asprovided by the standard decoding algorithms of that symbology (suchthat used in 2-D Data Matrix codes) is used to further weight signaturemetric data appropriately. If a data region within the symbol iscorrupted by damage to the mark and that region yields a disagreementwith stored signature data while other uncorrupt regions agree well, thevoting weight of the corrupted region may be diminished. This mechanismprevents detectable symbol corruptions from presenting a false-negativeresult in a candidate symbol metric comparison against the genuinesymbol signature data. The ISO 16022 “Data Matrix Symbol” specificationdescribes an example of how Error Correction Codes (ECC's) can bedistributed within a 2-D Data Matrix, and how corrupted and uncorruptedregions within a Data Matrix can be identified.

Magnitude Filtering

In steps 114 and 116, candidate signature features are evaluated toensure they possess adequate magnitude to act as a part of eachsignature metric. This step ensures that the features forming eachsignature metric possess a real “signal” to encode as a distinguishingcharacteristic of the mark. Failure to apply threshold minima tosignature contributor candidates can allow a signature that is easilysubsumed by noise in any subsequent attempts to validate a mark againstthe genuine stored signature, rendering the validation process highlysusceptible to the quality and fidelity limitations of the device(s)used to capture the mark data for signature analysis. By ensuring thatsignature metrics are formed solely of features satisfying thesemagnitude minima, the ability to perform successful verification of marksignatures with a wide variety of acquisition devices (camera-equippedcell phones, machine-vision cameras, low-quality or low-resolutionimagers, etc.) and in a wide range of ambient environments (varied, lowor non-uniform lighting, etc.) can be ensured or greatly facilitated.

In an embodiment, using a 2-D Data Matrix code as an example, in steps110, 112, and 114 candidate features for the four signature metrics 92,94, 96, 98 are extracted and sorted by magnitude. As previouslydescribed, the mark 20 is acquired such that the features can beprocessed in electronic form, typically as a color or gray-scale image.As a preliminary step, the 2-D Data Matrix is first analyzed as a wholeand a “best fit” grid defining the “ideal” positions of the boundariesbetween cells of the matrix is determined. Candidate features are thenselected by finding features that are most deviant from the “normal” or“optimum” state of the marks attribute(s) for the particular metricbeing analyzed. Considering the 2-D Data Matrix code example shown inFIG. 5, some suitable attributes are:

1. The modules 92 whose average color, pigmentation or mark intensityare closest to the global average threshold differentiating dark modulesfrom light modules as determined by the Data Matrix reading algorithms(i.e., the “lightest” dark modules and the “darkest” light modules).

2. The modules 94 that are marked in a position that is most deviantfrom the idealized location as defined by a best-fit grid applied to theoverall symbol 20. Two possible methods of identifying these modulesare: (a) extract the candidate mark module edge positions and comparethose edge positions to their expected positions as defined by anidealized, best-fit grid for the whole symbol 20; (b) extract ahistogram of the boundary region between two adjacent modules ofopposite polarity (light/dark or dark/light), with the sample regionoverlapping the same percentage of each module relative to the best-fitgrid, and evaluate the deviation of the histogram from a 50/50 bimodaldistribution.

3. The extraneous marks or voids 96 in the symbol modules 94, whetherthey are light or dark, are defined as modules possessing a wide rangeof luminance or pigment density. In other words, they are defined asmodules possessing pigmentation levels on both sides of the globalaverage threshold differentiating dark modules from light modules, withthe best signature candidates being those with bimodal luminancehistograms having the greatest distance between the outermost dominantmodes.

4. The shape of the long continuous edges 98 in the symbol, such astheir continuity/linearity or degree of discontinuity/non-linearity. Onemethod of measuring this attribute and extracting this data is bycarrying out a pixel-wide luminance value projection, with a projectionlength of one module, offset from the best fit grid by one-half module,run perpendicular to the grid line bounding that edge in the best-fitgrid for the symbol.

The 2-D Data Matrix makes a good example because it includes squareblack and white cells, in which the above described features are easilyseen. However, the same principles can of course be applied to otherforms of data-encoding or non-data-encoding visible mark.

Once candidate features complying with the above-described criteria havebeen identified, the candidate features are sorted in step 114 into alist in order of magnitude, and are then subjected in step 116 tomagnitude limit filtering by finding the first feature in each list thatdoes not satisfy the established minimum magnitude to qualify as acontributor to that metric. The threshold may be set at any convenientlevel low enough to include a reasonable number of features that cannoteasily be reproduced, and high enough to exclude features that are notreasonably durable, or are near the noise-floor of the image acquisitiondevice 58. In this embodiment, the low-magnitude end of the sorted listis then truncated from that point and the remaining (highest magnitude)features are stored, along with their locations in the mark, as thesignature data for that metric. Preferably, all features above thetruncation threshold are stored, and that implicitly includes in thesignature the information that there are no signature features above themagnitude filter threshold elsewhere in the mark.

As it is known beforehand that different marking device technologiespresent superior or inferior signature features in different attributesfor use in creating Metrics signature data, the marking device type maybe used to pre-weight the metrics in what is referred to as a WeightingProfile. For example, should the genuine marks be created using athermal transfer printer, it is known that edge projections parallel tothe substrate material direction of motion are unlikely to carry asignature magnitude sufficient to encode as part of the genuinesignature data. This knowledge of various marking device behaviors maybe used during the capture of the original genuine signature data. Ifemployed, all metrics used in the creation of the genuine mark signatureare weighted as appropriate for the known behaviors of that particularmarking device type, and the resulting emphasis/de-emphasis mapping ofthe metrics becomes a Metrics Weighting Profile. In step 118, thisprofile of the metrics weighting, based on the marking device type usedto create the original mark, is stored as part of the signature data.

In step 120, the signature metrics are stored as sorted lists offeatures, in descending order of magnitude. The list entry for eachfeature includes information localizing the position in the mark fromwhich that feature was extracted.

In this embodiment, the record for each symbol is indexed under a uniqueidentifier content (typically a serial number) included in theexplicitly encoded data in the symbol. The record may be stored on anetwork accessible data storage server or device, or may be storedlocally where it will be needed. Copies may be distributed to localstorage at multiple locations.

Low Amplitude Signature Metrics

In an embodiment, if the instance of a symbol 20 or an identifiableregion within the symbol 20 lacks any signature feature satisfying theminimum magnitude for one or more of the signature metrics, that factitself is stored as part of the signature data, thereby utilizing thelack of significant feature variation as part of the unique identifyinginformation for that symbol. In this case, a symbol subjected toverification against that data is considered genuine only if it alsopossesses zero signature features satisfying the minimum magnitude forthe metric(s) in question, or at least sufficiently few significantfeatures to pass a statistical test. In these cases, the weighting forthat particular metric is diminished, as a region with no distinguishingcharacteristics is a less robust identifying feature than would be aregion with significant distinguishing characteristics. A symbol orregion with no significant signature feature is most useful negatively.The absence of significant features from both the genuine mark 20 andthe candidate mark 30 is only weak evidence that the candidate mark isgenuine. The presence of a significant feature in a candidate mark 30,where the genuine mark 20 has no feature, is stronger evidence that thecandidate mark is counterfeit.

An exception is made for features of appreciable signature magnitudethat can be attributed to symbol damage in the candidate symbol 30,revealed via the aforementioned use of symbol Error Correctioninformation from the decoding algorithms of that particular symbology,and subject to the principles of captured image fidelity signaturemetrics weighting as previously described.

In the extreme case where both the genuine mark 20 and the candidatemark 30 contain ONLY sub-threshold data (as in 2 “perfect” symbols),they would be indistinguishable by the process of the present examplebecause that process relies on some measurable variation in either thegenuine or counterfeit mark to act as a way of detection. That is not aproblem in practice, as none of the usage scenarios presentlycontemplated (typically, on-line, high speed printing) produce perfectsymbols.

If necessary, for example, if the printing process used is too wellcontrolled to produce a sufficiency of measurable variations, then instep 102 marks 20 can be created with deliberately introduced random orquasi-random variations. Such variations can then be detected inconjunction with detecting the variations arising naturally from themark creation process in the manner described previously. For example,if the marks are printed on labels, a printer and label substrate may beused that produce marks of such high quality that the naturally arisingvariations are insufficient to reliably distinguish individual marksfrom each other. In that case, the printing process may be modified tointroduce random or quasi-random anomalies to the printed marks, so thatthe randomly introduced anomalies and the naturally arising variationstogether are sufficient to reliably distinguish individual marks fromeach other.

In contrast to methods that rely solely on deliberately applied securityfeatures, the present process needs only to add a minimum ofquasi-random features to fortify the naturally occurring variation. Inthis way, it is possible to create the conditions where the mark canthen satisfy enough of the magnitude filter threshold minima forcreating a usable signature. Such artifacts may be introduced into themark 20 using any appropriate method. For example, in exemplaryembodiments, the printer may be adapted to self-create the necessaryartifacts as part of the printing process, or the software thatgenerates the marks before printing may be modified to introduceartifacts, or the like. Thus, deliberately introduced artifacts canboost the performance of the herein described systems and methods whenusing low-variation marking technologies.

Analysis

Referring to FIG. 11, in the present embodiment, signature metrics arestored as a sorted list, in descending order of magnitude, and includeinformation localizing their position in the mark from which they wereextracted. In the preferred embodiment, using a 2-D Data Matrix code asan example, the process by which a candidate mark or symbol is evaluatedto determine if it is genuine is as follows:

In step 152, an image of the candidate mark 30 is acquired by the imageacquisition device 58.

In step 154, the explicit data in candidate mark 30 is decoded and itsUID content is extracted.

In step 156, the UID is used to look up the signature metric dataoriginally stored for the original symbol 20 having that UID. The storeddata may be retrieved from the local storage 64 or may be retrieved froma network accessible data storage server or long-term storage 72. In thecase of a candidate mark 30 that does not contain a UID, some otheridentifying information may be obtained relating to the candidate mark30. Alternatively, the entire database of genuine mark signatures onlocal storage 64 or long-term storage 72 may be searched after step 164below, to attempt to locate a genuine signature that matches candidatemark signature.

In step 158, in the case of a 2-dimensional barcode or other datacarrier for which a quality measure can be established, qualitymeasurements 158 for the candidate mark 30 may be obtained, similarly tothose obtained in step 108 for the genuine mark 20. The qualitymeasurements may be used in the subsequent analysis steps to reduce theweight given to a mark, or parts of a mark, that appear to have beendamaged since it was applied. Also, if the quality measurements of theoriginal symbol 20 were stored as part of the genuine signature data,the stored quality measurements can be verified against the signaturedata extracted from the candidate mark 30.

In step 160, significant signature features are extracted from the imageof candidate mark 30 that was acquired in step 152. The whole ofcandidate mark 30 (other than sections that have been disqualified ascorrupt because of ECC errors) is searched for significant features. Inaddition, the information specifying the locations within the symbolfrom which the original, genuine symbol signature data was extracted isused to specify from where to extract the signature data from thecandidate symbol. That ensures that a feature present in mark 20 butabsent from mark 30 is noted.

In step 162, the signature features are encoded for analysis.

In step 164, the signature data extracted from the candidate symbol 30is sorted into the same order (for example, magnitude-sorted) as theoriginal list of the original symbol 20.

In steps 166, the candidate signature data is compared to the storedoriginal signature data. The data is subjected to a statisticaloperation revealing numeric correlation between the two data sets. Eachmetric is subjected to individual numerical analysis yielding a measurereflecting the individual confidence of the candidate symbol as beingthe genuine item for that metric. If the mark does not contain UID data,and no alternative identifying data is available, it may be necessary tosearch through a database of similar marks, using the proceduresdiscussed with reference to FIG. 13 below. For example, in the case ofFIGS. 1 and 3, it may be necessary to search through all genuine marks20 that have the same overt pattern of black and white modules. Theobjective of the search is to identify, or fail to identify, a singlegenuine mark 20 that is uniquely similar to the candidate mark 30.

In step 168, where the Metrics Weighting Profile was stored as part ofthe genuine signature data, this information is used to emphasize and/orde-emphasize metrics as appropriate for the type of marking device usedto create the original genuine marks.

In step 170, where the image acquisition devices 58 used in steps 104and 152 have different sensitivities, the contributions of signaturedata to the overall analysis result may need to be adjusted. Forexample, the minimum magnitude threshold used for significant featuresmay need to be set at a level appropriate for the less sensitive imageacquisition device 58, or a particular metric may need to be omittedfrom the analysis set as it is known not to carry adequate signaturemagnitude in marks produced by the original marking device. In somecases, a feature that is recognized in one of the higher resolutioncategories in the scale shown in FIG. 10 may be mistaken by alower-resolution scanner for a feature in a different category. Forexample, a feature that is seen at high resolution as a black modulewith a white void may be seen at low resolution as a “low pigmentationmodule.” In general, the resolution of the verification scanner 58 isused in conjunction with the marking device Metrics Weighting Profile todetermine what metrics to emphasize/de-emphasize. In this example, inthe low resolution image the feature could exist in the “low pigment”list, but would exist in both the “low pigment” and “void” lists in thehigh resolution image. Since the methods used are ultimately subjectedto statistics-based analytics, the occasional occurrence of a minor markthat fell below the resolution of the original scan will be ofnegligible impact. This is because, even though such a mark is notresolved as an “object,” its effect will be captured in at least one ofthe metrics employed (such as reduced module gray level as in thisexample). That has proven true in practical trials even when using scanresolutions up to 2× higher in the verification image as was used in theoriginal signature scan.

If it is desired to correct explicitly for the resolution of theoriginal and/or verification scan, in many cases the resolution can bedetermined at verification time by detecting a comparatively abrupt dropin the number of artifacts at the scanner's resolution threshold.Alternatively, where the original scanner may be of lower resolutionthan the verification scanner, the resolution of the scan, or otherinformation from which the resolution can be derived, may be included asmetadata with the stored signature, similarly to the Metrics WeightingProfile discussed above. Whatever procedure is used, sorting thesignature data in order by magnitude of the artifact makes it very easyto apply or change a threshold magnitude.

In step 172, by exclusion, all locations within a mark not representedin the sorted list of feature locations satisfying the minimum magnitudethreshold are expected to be devoid of significant signature featureswhen analyzing a genuine mark. This condition is evaluated by examiningthe signature feature magnitude at all locations within a candidate markwhere sub-threshold features are expected and adjusting the results forthe appropriate metric toward the negative when features exceeding thethreshold minimum are found. If the significant features are found in aregion determined to have been damaged when evaluated for symbol errorcorrection or other quality attributes, the adjustment is diminished ornot carried out at all depending on the location of the damage relativeto the feature extraction point and the nature of the particular metricinvolved. For example, if a discrepancy in a signature feature relativeto the original mark 20 is extracted from a module of the candidate mark30 that is near, but not the same as, the damaged module(s), thenegative adjustment to the metric because of that feature may bediminished by a proportion that reflects reduced confidence in themetric signature. This is because the former module, being near a knowndamaged region, may well have suffered damage that affects the metricbut falls below the detectable threshold of the quality or ECCevaluation mechanism of the symbology. If the discrepancy is extracteddirectly from a damaged module, or if the metric is one of the typesthat spans multiple modules and that span includes the damaged one, theadjustment will not be applied at all.

In step 174, these individual confidence values are then used todetermine an overall confidence in the candidate symbol 30 as genuine(or counterfeit), with the individual confidence values being weightedappropriately as described above using image fidelity, resolution andsymbol damage information.

In step 176, it is determined whether the result is sufficientlydefinite to be acceptable. If the comparison of the signature datayields an indeterminate result (for example, the individual metricshaving contradictory indications not resolvable through the use of thedata weighting mechanism), the user submitting the symbol forverification is prompted to re-submit another image of the symbol forprocessing, and the process returns to step 152.

For practical reasons, the number of permitted retries is limited. Instep 178, it is determined whether the retry limit has been exceeded. Ifso, a further return for rescanning is prevented.

Once the analysis has been completed successfully, the results of thecomparison analysis are reported in step 180. The report may bepass/fail, or may indicate the level of confidence in the result. Theseresults may be displayed locally or transferred to a networked computersystem or other device for further action. If the result is stillindeterminate when the retry limit is reached, that also proceeds tostep 178, where the indeterminate result may be reported as such.

Upon the storing of the signature data extracted from the mark 20 shownin FIG. 1, the present embodiment is capable of recognizing that samemark as genuine when presented as a candidate mark 30 by virtue of thefact that, when analyzed by the same process, the candidate mark 30 isdetermined to possess the same signature data (at least to a desiredlevel of statistical confidence). Similarly, the present embodiment iscapable of identifying a counterfeit copy 30 of the mark 20 shown inFIG. 1 or distinguishing a different unique instance 30 of the mark byrecognizing that the signature data (e.g., as extracted from theinstance of the mark in FIG. 3) does not match the signature dataoriginally stored from when the genuine mark shown in FIG. 1 wasoriginally processed.

Operation on Distorted Substrates

In developing the signature metrics in an embodiment, immunity todistortions of the substrate upon which the analyzed marks are made maybe important. Module luminance or color, module grid position bias, voidor mark locations and edge profile shape are properties where theextraction methods employed can be made largely immune to signature dataimpacts caused by presentation on distorted substrates. This isaccomplished by using feature extraction methods that dynamically scaleto the presented mark geometry independent of changes in mark aspectratio. The primary mechanism for this in an embodiment is the creationof the best-fit grid for the candidate mark at the start of extraction.This is especially important in the case where the genuine mark 20 ismade on a label travelling on a flat label web and the label is thenapplied to an object that is not flat, such as a bottle with a curvedsurface. The candidate marks 30 submitted for analysis to check theirstatus as genuine or counterfeit may be acquired for processing while onthe non-flat surface (e.g., a round bottle). The ability to verifysymbols presented on various substrate geometries with minimum impact onthe reported signature metrics represents a significant advantage to themethods described herein.

Local Reference Measurements for Metric Data for Environmental Immunity

To further make the extraction of accurate signature data robust in anembodiment, various methods described herein may utilize area-localreferencing within the analyzed symbol for composing the signature data.This provides greater immunity to things like the aforementionedsubstrate distortion, non-uniform lighting of the candidate symbol whenacquired for processing, non-ideal or low quality optics in theacquiring device, or many other environmental or systematic variables.In an embodiment, the metric reference localizations are:

1. Average module color, pigmentation or mark intensity reference thenearest neighbor(s) of the opposite module state (dark vs. light orlight vs. dark). Where a cell is identified as a significant feature 92with deviant average pigmentation density, the cells for which it was anearest neighbor may need to be reassessed discounting the identifieddeviant cell as a reference.

2. Module grid position bias is referenced to the overall symbol bestfit grid, and as such has native adaptive reference localization.

3. The analysis of extraneous marks or voids in the symbol modules usesmodule-local color, pigmentation or mark intensity references. In otherwords, the image luminance histogram within the analyzed module itselfprovides reference values for the applied methods.

4. The projection methods used to extract the shapes of long continuousedges in the symbol are differential in nature and have native immunityto typical impacting variables.

FIG. 12 depicts an alternative embodiment that is similar to the processdescribed with reference to FIG. 5, but that may use types of mark otherthan the 2-D symbol. For instance, the symbol may be a 1-D linearbarcode, a company logo, etc. FIG. 12 shows some features of a 1-Dlinear barcode 200 that may be used as signature metrics. These include:variations in the width of and/or spacing between bars 202; variationsin the average color, pigmentation or intensity 204; voids in black bars206 (or black spots in white stripes); or irregularities in the shape ofthe edges of the bars 208.

Analysis by the Autocorrelation Method

In the embodiments described above, the raw list of data for each metricmay first be array-index matched and subjected to normalized correlationto a like-order extracted metric set from a candidate symbol. Thesecorrelation results are then used to arrive at a match/no match decision(genuine vs. counterfeit). To do that, storage of the signature includesthe sorting order of the original genuine symbol modules as well as thetrained metrics values themselves, complete for each metric. In additionto the exhaustive storage need, the raw data is not “normalized,”because each metric has its own scale, sometimes unbounded, whichcomplicates the selection of storage bit-depths. A typicalimplementation of the above-described embodiments has a stored signaturesize of approximately 2 kilobytes.

Referring now to FIGS. 13 to 17, an alternative embodiment of metricspost-processing, storage and comparison methods is applied after theoriginal artifact metrics have been extracted and made available as anindex-array associated list (associable by module position in thesymbol). Based on autocorrelation, the application of this newpost-processing method can, in at least some circumstances, yieldseveral significant benefits when compared to the signatures of theprevious embodiments. Most significant is a reduction in data packagesize. For example, a 75% reduction in the stored signature data has beenrealized. Even more (up to 90% reduction) is possible with theapplication of some minor additional data compression methods. Thisdramatic reduction arises from the use of autocorrelation, list sorting,and the resultant normalization and data-modeling opportunities thesemechanisms allow to be applied to the original artifacts' data.

Where in the embodiments described above the analysis of a particularset of metrics data takes the form of comparing the sorted raw metricsextracted from a candidate symbol to the like-ordered raw metricsextracted from the genuine symbol, the autocorrelation method comparesthe autocorrelation series of the sorted candidate symbol metrics datato the autocorrelation series of the (stored) sorted genuine symboldata—effectively we now correlate the autocorrelations. In anembodiment, the Normalized Correlation Equation is used:

$r_{xy} = \frac{{n{\sum{x_{i}y_{i}}}} - {\sum{x_{i}{\sum y_{i}}}}}{\sqrt{{n{\sum x_{i}^{2}}} - \left( {\sum x_{i}} \right)^{2}}\sqrt{{n{\sum y_{i}^{2}}} - \left( {\sum y_{i}} \right)^{2}}}$where r is the correlation result, n is the length of the metric datalist, and x and y are the Genuine and Candidate metrics data sets. Whenthe operation is implemented as an autocorrelation, both data sets x andy are the same.

To produce the autocorrelation series, the correlation is performedmultiple times, each time offsetting the series x by one additionalindex position relative to the series y (remembering that y is a copy ofx). As the offset progresses the data set must “wrap” back to thebeginning as the last index in they data series is exceeded due to the xindex offset; this is often accomplished most practically by doublingthe y data and “sliding” the x data from offset 0 through offset n togenerate the autocorrelation series.

In implementing the autocorrelation approach, the first benefit observedis that it is not necessary to store the signature data valuesthemselves as part of the stored data. In autocorrelation, a data seriesis simply correlated against itself. So, where previously it wasnecessary to deliver both the extraction (sort) order and genuinesignature data values to the verification device for validation, nowonly the sort/extraction order for the autocorrelation series operationneed be provided.

The genuine autocorrelation signature needed to compare to the candidatesymbol results does not require storing or passing the genuine data tothe verifier. Because the operation of generating the signature isalways performed on sorted metrics data, the autocorrelation series forthe original artifacts' information is always a simple polynomial curve.Therefore, rather than needing to store the entire autocorrelationseries of each genuine symbol metric, it is sufficient to store a set ofpolynomial coefficients that describe (to a predetermined order andprecision) a best-fit curve matching the shape of the genuineautocorrelation results for each metric.

In an embodiment, r_(xy) is computed, where each term x_(i) is anartifact represented by its magnitude and location, and each termy_(i)=x_((i+j)), where j is the offset of the two datasets, for j=0 to(n−1). Because the x_(i) are sorted by magnitude, and the magnitude isthe most significant digits of x_(i), there is a very strong correlationat or near j=0, falling off rapidly towards j=n/2. Because y is a copyof x, j and n−j are interchangeable. Therefore, the autocorrelationseries always forms the U-shaped curve shown in FIG. 13, which isnecessarily symmetric about j=0 and j=n/2. It is therefore onlynecessary to compute half of the curve, although in FIG. 13 the wholecurve from j=0 to j=n is shown for clarity.

In practice, it has been found that a 6th order equation using 6 bytefloating point values for the coefficients always matches the genuinedata to within 1% curve fit error or “recognition fidelity.” That is tosay, if a candidate validation is done using the actual autocorrelationnumbers and then the validation is done again on the same mark using thepolynomial-modeled curve, the match scores obtained will be within 1% ofeach other. That is true both of the high match score for a genuinecandidate mark and of the low match score for a counterfeit candidatemark. That allows a complete autocorrelation series to be representedwith only 7 numbers. Assuming that 100 data points are obtained for eachmetric, and that there are 6 metrics (which have been found to bereasonable practical numbers), that yields a reduction of 600 datavalues to only 42, with no loss of symbol differentiability or analysisfidelity. Even if the individual numbers are larger, for example, if the600 raw numbers are 4 byte integers and the 42 polynomial coefficientsare 6 byte floating point numbers, there is a nearly 90% data reduction.In one experimental prototype, 600 single byte values became 42 4-bytefloats, reducing 600 bytes to 168 bytes, a 72% reduction.

Further, the stored signature data is now explicitly bounded andnormalized. The polynomial coefficients are expressed to a fixedprecision, the autocorrelation data itself is by definition alwaysbetween −1 and +1, and the sort order list is simply the module arrayindex location within the analyzed symbol. For a 2-D data matrix, themodule array index is a raster-ordered index of module position within asymbol, ordered from the conventional origin datum for that symbology,and thus has a maximum size defined by the definition of the matrixsymbology. In one common type of 2-D data matrix, the origin is thepoint where two solid bars bounding the left and bottom sides of thegrid meet. There is also established a standard sorted list length of100 data points for each metric, giving a predictable, stable andcompact signature.

In an embodiment, the comparison of a genuine signature to a candidatenow begins with “reconstituting” the genuine symbol autocorrelationsignature by using the stored polynomial coefficients. Then, the rawmetrics data is extracted from the candidate symbol, and is sorted inthe same sort order, which may be indicated as part of the genuinesignature data if it is not predetermined.

The candidate metrics data is then autocorrelated. The resultantautocorrelation series may then be correlated against the reconstitutedgenuine autocorrelation curve for that metric, or alternatively the twocurves may be compared by computing a curve-fit error between the pair.This correlation is illustrated graphically in FIGS. 13 and 16. Thisfinal correlation score then becomes the individual “match” score forthat particular metric. Once completed for all metrics, the “match”scores are used to make the genuine/counterfeit decision for thecandidate symbol.

Additionally, use can further be made of the autocorrelation curves byapplying power-series analysis to the data via discrete Fouriertransform (DFT):

$X_{k} = {\sum\limits_{n = 0}^{N - 1}{x_{n} \cdot e^{{- i}\; 2\;\pi\; k\;{n/N}}}}$where X_(k) is the k^(th) frequency component, N is the length of themetric data list, and x is the metrics data set.

The Power Series of the DFT data is then calculated. Each frequencycomponent, represented by a complex number in the DFT series, is thenanalyzed for magnitude, with the phase component discarded. Theresulting data describes the distribution of the metric data spectralenergy, from low to high frequency, and it becomes the basis for furtheranalysis. Examples of these power series are shown graphically in FIGS.14, 15, and 17.

Two frequency-domain analytics are employed: Kurtosis and a measure ofenergy distribution around the center band frequency of the totalspectrum, referred to as Distribution Bias. Kurtosis is a commonstatistical operation used for measuring the “peakedness” of adistribution, useful here for signaling the presence of tightly groupedfrequencies with limited band spread in the power series data. In anembodiment, a modified Kurtosis function may be employed as follows:

${kurtosis} = \frac{\sum\limits_{n = 1}^{N}\left( {Y_{n} - \overset{\_}{Y}} \right)^{4}}{{N\left( {N - 1} \right)}s^{4}}$where Y is the mean of the power series magnitude data, s is thestandard deviation of the magnitudes, and N is the number of analyzeddiscrete spectral frequencies.

The Distribution Bias is calculated as

${DB} = \frac{{\sum\limits_{n = 0}^{{({N/2})} - 1}x_{n}} - {\sum\limits_{n = {N/2}}^{N}x_{n}}}{\sum\limits_{n = 0}^{N}x_{n}}$where N is the number of analyzed discrete spectral frequencies.

The smooth polynomial curve of the genuine symbol metric signatures(arising from the by-magnitude sorting) yields recognizablecharacteristics in the spectral signature when analyzed in the frequencydomain. A candidate symbol, when the metrics data are extracted in thesame order as prescribed by the genuine signature data, will present asimilar spectral energy distribution if the symbol is genuine. In otherwords, the genuine sort order “agrees” with the candidate's metricmagnitudes. Disagreement in the sorted magnitudes, or other superimposedsignals (such as photocopying artifacts), tend show up as high-frequencycomponents that are otherwise absent in the genuine symbol spectra, thusproviding an additional measure of symbol authenticity. This addressesthe possibility that a counterfeit autocorrelation series might stillsatisfy the minimum statistical match threshold of the genuine symbol.This is a remote possibility, but can conceivably happen when usingnormalized correlation if the overall range of the data is largecompared to the magnitude of the errors between individual data pointsand the natural sort order of the dominant metric magnitudes happens tobe close to that of the genuine symbol. The distribution characteristicsof the DFT power series of such a signal will reveal the poor quality ofthe match via the high frequencies present in the small amplitude matcherrors of the candidate series. Such a condition could be indicative ofa photocopy of a genuine symbol. In specific terms, here we expect ahigh Kurtosis and a high Distribution Ratio in the spectra of a genuinesymbol.

Along with the autocorrelation match score, one can make use of thispower series distribution information as a measure of “confidence” inthe verification of a candidate symbol.

FIG. 13 shows a comparison of the autocorrelation series for a singlemetric between a genuine item (polynomial approximation) and a candidatesymbol (genuine in this case). Note the close agreement—here thecorrelation between the two autocorrelation series exceeds 93%.

FIG. 14 is a power series from the original genuine autocorrelation dataused for FIG. 13. It can clearly be seen that the spectrum is dominatedby low frequencies.

FIG. 15 is a power series similar to FIG. 14 from a cell phone acquiredimage of the genuine item of FIG. 14. Some image noise is present, butthe overall power spectrum closely matches the genuine spectrum, withthe same dominance of low frequency components.

FIG. 16 shows a comparison of the autocorrelation series for a singlemetric between the polynomial approximation for a genuine item and acandidate symbol (here a counterfeit). There is considerabledisagreement, and the candidate autocorrelation is noticeably morejagged than in FIG. 13. The numeric correlation between the two seriesis low (<5%), and the jagged shape of the data is also apparent in theDFT analysis (below).

FIG. 17 shows the power series from the cell phone acquired image of thecounterfeit symbol of plot 4. Note how the low frequency components arediminished with the total spectral energy now spread out to includesignificant portions of the higher frequency range.

Alternate Embodiment Using String Literal Comparisons

Referring now to FIG. 18, in some implementations it is desirable toavoid the use of computationally intensive methods such as numericcorrelation or other statistical operations. In other instances, themark being used for signature extraction may not be a data carryingsymbol, or may be a symbol with limited data capacity, that does notallow for the association of the mark signature metrics with a uniqueidentifier, such as a serial number. In an alternate embodiment, thesignature data for the mark may be encoded as a string of bytes, whichmay be visualized as ASCII characters, rather than the numeric magnitudedata used in the above example. This alternate data format provides theability to use the signature data directly to look-up a particular mark(e.g., in a database) as would normally be done using a serial number inthe case of a data carrying symbol. When encoding the mark data as aliteral string of ASCII characters, the signature ASCII data itselfbecomes the unique identifier information for the mark, acting as woulda serial number for example as in the case of a data carrying symbol.

In this embodiment, rather than storing the location and magnitude ofeach signature metric for a mark, what is stored is the presence (orabsence) of significant signature features and each of the evaluatedlocations within a mark. For example, in the case of a 2-D Data Matrixsymbol that does not carry/encode a unique identifier/serial number, thesignature data may be stored as a string of characters, each encodingthe presence/absence of a feature exceeding the minimum magnitudethreshold for each signature metric in a module, but not encodingfurther data about the magnitude or number of features in any onemetric. In this example, each module in the symbol has 4 bits of data,one bit for each of the signature metrics, where a “1” indicates thatthe particular metric signature has a significant feature at that modulelocation. Therefore, in this example, every possible combination of thefour metrics extracted and tested against the magnitude limit minima maybe encoded in one half byte per module: 0000 (hexadecimal 0) through1111 (hexadecimal F), with 0000 meaning that none of the testedsignature metrics are present to a degree greater than the magnitudeminimum in that particular module and 1111 meaning that all four of thetested signature metrics are present to a degree greater than themagnitude minimum in that particular module.

In the example of a 2-D data matrix 250 shown in FIG. 18, the first sixmodules are coded as follows. A first module 252 has no artifact foraverage luminance: it is satisfactorily black. It has no grid bias. Itdoes have a large white void. It has no edge shape artifact: its edgesare straight and even. It is thus coded 0010. A second module 254 has avoid and an edge shape artifact, and is coded 0011. A third module 256is noticeably gray rather than black, but has no other artifacts, and iscoded 1000. A fourth module 258 has no artifacts, and is coded 0000. Afifth module 260 has a grid bias but no other artifacts, and is coded0100. A sixth module has no artifacts, and is coded 0000. Thus, thefirst six modules are coded as binary 00100011 10000000 01000000, orhexadecimal 238040, or decimal 35-128-64, or ASCII # € @. (Some ASCIIcodes, especially those in the extended range from decimal 128-255, havevariable character assignments. That is not important for the presentimplementation, because they are never actually expressed ashuman-readable characters.)

Analysis Under the String Literal Encoding Embodiment

Signature metrics of the genuine mark are stored as an ASCII string,encoding the signature data as described above. Using a 2-D Data Matrixcode as an example, with a typical symbol size of 22×22 modules, theASCII string portion containing the unique signature data would be 242characters in length, assuming the data is packed 2 modules percharacter (byte). The signature strings of genuine marks are stored in adatabase, flat file, text document or any other construct appropriatefor storing populations of distinct character strings. The stored datamay be on local storage where it is expected to be needed, or may besearchable over a network on any connected data storage server ordevice.

In this example, the process by which a candidate mark is evaluated todetermine if it is genuine is as follows:

1. The candidate symbol is analyzed and its signature ASCII stringextracted.

2. This signature string is used as a search query of the stored genuinesignature data to attempt to find a match within the genuine signaturedata set.

3. The stored data is subjected to a test for an exact match of thecomplete candidate search string. If a complete string match is notfound, an approximate match may be sought, either by searching forsub-strings or by a “fuzzy match” search on the whole strings.Algorithms to search a candidate string against a database of referencestrings, and return the identity of the best match(es) and thepercentage identity, are well known and, in the interests ofconciseness, will not be further described here.

4. Where the search returns a match to one reference string of at leasta first, minimum confidence match threshold, the original and candidatesymbols may be accepted as the same. Where the search returns no stringwith a percentage match above a second, lower threshold, the candidatesymbol may be rejected as counterfeit or invalid.

5. Where the search returns one reference string with a percentage matchbetween the first and second thresholds, the result may be deemed to beindeterminate. Where the search returns two or more reference stringswith a percentage match above the second threshold, the result may bedeemed to be indeterminate, or a further analysis may be conducted tomatch the candidate string with one or other of the reference strings.

6. When the result is indeterminate, the user submitting the symbol forverification may be prompted to re-submit another image of the symbolfor processing. Instead, or in addition, the signature extraction methodmay employ a retry method for encoding the individual features in theoriginal image. The retry method may be applied to any module whosesignature data in the candidate symbol is close to the magnitude minimumthreshold for that metric. (In this embodiment, the signature datamagnitude is not available for the stored original symbols.) If thesymbol being analyzed uses an error correction mechanism, the retrymethod may be applied to any module in a symbol, or part of the symbol,that the error correction mechanism indicates as possibly damaged oraltered. Instead, or in addition, any signature data with a magnitudethat is close to that minimum magnitude threshold may be de-emphasized,for example, by searching with its presence bit asserted (set to 1) andthen again with the bit un-asserted (set to 0), or by substituting a“wild-card” character. Alternatively, the percentage match query may berecomputed giving reduced or no weight to those bits representingfeatures that are close to the threshold.

7. Once completed successfully, the results of the comparison analysisare reported. These results may be displayed locally or transferred to anetworked computer system or other device for further action.Indeterminate results may be reported as such.

The advantages of embodiments described herein include, withoutlimitation, the ability to uniquely identify an item by using a markthat has been placed on the item for another purpose, without the needto specifically introduce overt or covert elements for the purposes ofanti-counterfeiting. A further advantage is that such identification canbe very difficult to counterfeit. Further advantages include the abilityto integrate the functions of the present disclosure into existingtechnologies commonly used to read barcode symbols, such as machinevision cameras, bar code readers and consumer “smart phones” equippedwith cameras, without altering the primary behavior, construction orusability of the devices. Another advantage, in the case of a2-dimensional barcode for example, is the ability to use the signaturedata as a means of providing a redundant data-carrier for the purpose ofidentifying an item.

In an instance where damage to the candidate mark makes it onlypartially readable, or makes it impossible to read and/or decode adata-carrying symbol, or the like, undamaged identifying features ofonly a portion of the mark may be sufficient to identify the mark. Oncethe candidate mark is thus identified with a genuine mark, the signatureof the genuine mark can be retrieved from storage, and any informationthat was incorporated into the signature, such as a serial number of themarked item, may be recovered from the retrieved signature instead ofdirectly from the damaged mark. Thus, the signature data, either incombination with partially recovered encoded symbol information or not,can be used to uniquely identify an item. This has many advantages,particularly considering how a data carrying mark may be damaged duringa marked item's transit through a manufacturer's supply chain. Thischallenge has commonly been addressed by ensuring a data carrier iscreated with a very high quality or “grade” at the point of marking. Thegoal was to produce a mark of such high quality that it will still befully readable even after undergoing significant degradation due tophysical damage in the supply chain. That put an excessive burden ofcost and reduced manufacturing yields on the producer of the item as heendeavored to ensure that only marks of the highest quality entered hissupply chain. The present embodiment has the advantage of removing theneed for producing marks of the highest quality while still providing away of identifying unreadable marks that cannot be decoded in the normalway because of symbol damage.

As mentioned above, a symbol other than a 1-D or 2-D barcode may be usedas a target symbol. A company logo, for example, may function as thetarget symbol. The features, and the specific variations in thosefeatures, that are used as signature metrics are almost limitless. Insome embodiments, the mark need not be applied with a view to extractingsignature data according to the present methods. Instead, a mark thathad already been created could be used, provided that it containssuitable artifact features.

Where an original mark is applied to an original item, and/or anoriginal item is appended to an original object, the mark or item maycontain information about the item or object. In that case, theabove-described methods and systems may include verifying informationabout the item or object that is included in the mark or item, even whenthe underlying item or object is not physically replaced or altered. Forexample, where an object is marked with an expiry date, it may bedesirable to reject an object with an altered expiry date as “notauthentic” even if the object itself is the original object. Embodimentsof the present systems and methods will produce that result, if theartifacts used for verification are found in the expiry date, forexample, as imperfections of printing. Other information such as lotnumbers and other product tracking data may similarly be verified.

Various embodiments have been described in terms of acquiring an entire2-D barcode for signature data. However, the mark may be divided intosmaller zones. Where the original mark is large enough, and has enoughartifacts that are potential signature data, only one, or fewer thanall, zones may be acquired and processed. Where more than one zone isacquired and processed, the signature data from different zones may berecorded separately. That is especially useful if the mark is a symbolencoding data with error correction, and the error correction relates tozones smaller than the entire symbol. Then, if the error correctionindicates that part of the candidate symbol is damaged, the signaturedata from the damaged part can be disregarded.

Although the embodiments have been described primarily in terms ofdistinguishing an original mark (and by implication an original item towhich that mark is applied or attached) from a counterfeit copy of themark, the present methods, apparatus, and products may be used for otherpurposes, including distinguishing between different instances of theoriginal mark (and item).

In the interests of simplicity, specific embodiments have been describedin which the artifacts are defects in printing of a printed mark,applied either directly to the item that is to be verified, or to alabel applied to an object that is to be verified. However, as hasalready been mentioned, any feature that is sufficiently detectable andpermanent, and sufficiently difficult to duplicate, may be used.

Some of the embodiments have been described as using a database ofsignature data for genuine items, within which a search is conducted fora signature data that at least partially matches the signature dataextracted from a candidate mark. However, if the candidate item isidentified as a specific genuine item in some other way, a search may beunnecessary, and the signature data extracted from the candidate markmay be compared directly with the stored signature data for the specificgenuine item.

In view of the many possible embodiments to which the principles of thepresent discussion may be applied, it should be recognized that theembodiments described herein with respect to the drawing figures aremeant to be illustrative only and should not be taken as limiting thescope of the claims. Therefore, the techniques as described hereincontemplate all such embodiments as may come within the scope of thefollowing claims and equivalents thereof.

What is claimed is:
 1. A method for determining whether a candidatebarcode is genuine, the method comprising: acquiring an image of anoriginal barcode, wherein the original barcode includes a continuousedge; determining, from the image of the original barcode, a magnitudeof deviation of the continuous edge of the original barcode from anominal shape of the continuous edge of the original barcode; encodingthe magnitude of deviation of the continuous edge of the originalbarcode as signature data for the original barcode; storing thesignature data for the original barcode on a storage device; acquiringan image of the candidate barcode, wherein the candidate barcodeincludes a continuous edge; determining, from the image of the candidatebarcode, a magnitude of deviation of the continuous edge of thecandidate barcode from a nominal shape of the continuous edge of thecandidate barcode; encoding the magnitude of deviation of the continuousedge of the candidate barcode as signature data for the candidatebarcode; retrieving the signature data for the original barcode from thestorage device; comparing the signature data for the original barcodewith the signature data for the candidate barcode; and making adetermination that the candidate barcode is genuine or not genuine basedon a result of the comparison.
 2. The method of claim 1, furthercomprising applying the original barcode to an item prior to acquiringthe image of the original barcode.
 3. The method of claim 2, whereinapplying the original barcode to an item comprises applying the barcodeto a label, the method further comprising applying the label to anobject.
 4. The method of claim 2, wherein applying the original barcodeto an item comprises applying the barcode to an object.
 5. The method ofclaim 1, wherein acquiring the image of the candidate barcode comprisesreceiving the image of the candidate barcode from a cell phone.
 6. Themethod of claim 1, further comprising reading the original barcode toobtain an identifier, wherein storing the signature data for theoriginal barcode on the storage device comprises indexing the signaturedata for the original barcode using the identifier.
 7. The method ofclaim 1, further comprising reading the original barcode to obtain anidentifier, wherein retrieving the signature data for the originalbarcode comprises using the identifier to look up the signature data forthe original barcode.
 8. A system for determining whether a candidatebarcode is genuine, the system comprising at least one processorconfigured to carry out steps comprising: acquiring an image of anoriginal barcode, wherein the original barcode includes a continuousedge; determining, from the image of the original barcode, a magnitudeof deviation of the continuous edge of the original barcode from anominal shape of the continuous edge of the original barcode; encodingthe magnitude of deviation of the continuous edge of the originalbarcode as signature data for the original barcode; storing thesignature data for the original barcode on a storage device; acquiringan image of the candidate barcode, wherein the candidate barcodeincludes a continuous edge; determining, from the image of the candidatebarcode, a magnitude of deviation of the continuous edge of thecandidate barcode from a nominal shape of the continuous edge of thecandidate barcode; encoding the magnitude of deviation of the continuousedge of the candidate barcode as signature data for the candidatebarcode; retrieving the signature data for the original barcode from thestorage device; comparing the signature data for the original barcodewith the signature data for the candidate barcode; and making adetermination that the candidate barcode is genuine or not genuine basedon a result of the comparison.
 9. The system of claim 8, whereinacquiring the image of the candidate barcode comprises receiving theimage of the candidate barcode from a cell phone.
 10. The system ofclaim 8, further comprising reading the original barcode to obtain anidentifier, wherein storing the signature data for the original barcodeon the storage device comprises indexing the signature data for theoriginal barcode using the identifier.
 11. The system of claim 8,further comprising reading the original barcode to obtain an identifier,wherein retrieving the signature data for the original barcode comprisesusing the identifier to look up the signature data for the originalbarcode.